The Stanford Machine Learning Course Exercises repository contains programming assignments from the well-known Stanford Machine Learning online course. It includes implementations of a variety of fundamental algorithms using Python and MATLAB/Octave. The repository covers a broad set of topics such as linear regression, logistic regression, neural networks, clustering, support vector machines, and recommender systems. Each folder corresponds to a specific algorithm or concept, making it easy for learners to navigate and practice. The exercises serve as practical, hands-on reinforcement of theoretical concepts taught in the course. This collection is valuable for students and practitioners who want to strengthen their skills in machine learning through coding exercises.
Features
- Contains programming exercises from Stanford’s Machine Learning course
- Implements algorithms in Python and MATLAB/Octave
- Covers supervised learning methods including regression and classification
- Includes unsupervised learning methods such as clustering and PCA
- Provides neural network training and optimization examples
- Features recommender systems and anomaly detection exercises
Project Samples
